import operator_benchmark as op_bench
import torch
import torch.nn.qat as nnqat
import numpy
from pt import configs
from torch.ao.quantization import default_embedding_qat_qconfig
"""
Microbenchmarks for QAT Embedding + EmbeddingBag operators.
"""

class QATEmbeddingBagBenchmark(op_bench.TorchBenchmarkBase):
    def init(self, embeddingbags, dim, mode, input_size, offset, sparse, include_last_offset, device):
        qconfig = default_embedding_qat_qconfig
        self.embedding = nnqat.EmbeddingBag(
            num_embeddings=embeddingbags,
            embedding_dim=dim,
            mode=mode,
            include_last_offset=include_last_offset,
            sparse=sparse, device=device, qconfig=qconfig)
        numpy.random.seed((1 << 32) - 1)
        offsets = torch.LongTensor([offset], device=device)
        input = torch.tensor(numpy.random.randint(0, embeddingbags, input_size), device=device).long()
        self.inputs = {
            "input": input,
            "offset": torch.cat((offsets, torch.tensor([input.size(0)], dtype=torch.long)), 0)
        }
        self.set_module_name('qatEmbeddingBag')

    def forward(self, input, offset):
        return self.embedding(input, offset)

# Currently, EmbeddingBag QAT does not support sparse embeddings.
embeddingbag_short_dense_configs = [config for config in configs.embeddingbag_short_configs
                                    if {'sparse': True} not in config]

op_bench.generate_pt_test(embeddingbag_short_dense_configs, QATEmbeddingBagBenchmark)
op_bench.generate_pt_gradient_test(embeddingbag_short_dense_configs, QATEmbeddingBagBenchmark)

class QATEmbeddingBenchmark(op_bench.TorchBenchmarkBase):
    def init(self, num_embeddings, embedding_dim, input_size, device):
        qconfig = default_embedding_qat_qconfig
        self.embedding = nnqat.Embedding(
            num_embeddings=num_embeddings,
            embedding_dim=embedding_dim,
            qconfig=qconfig, device=device)
        self.embedding.qconfig = default_embedding_qat_qconfig
        numpy.random.seed((1 << 32) - 1)
        self.input = torch.tensor(numpy.random.randint(0, num_embeddings, input_size),
                                  device=device).long()
        self.inputs = {"input": self.input}
        self.set_module_name('qatEmbedding')

    def forward(self, input):
        return self.embedding(input)


op_bench.generate_pt_test(configs.embedding_short_configs, QATEmbeddingBenchmark)
op_bench.generate_pt_gradient_test(configs.embedding_short_configs, QATEmbeddingBenchmark)

if __name__ == "__main__":
    op_bench.benchmark_runner.main()
